Atlantic Oceanographic and Meteorological Laboratory (NOAA), Miami, FL
Joseph N. Boyer
Florida International University, Miami, FL
Light attenuation in marine ecosystems is of great importance to primary producers since light availability potentially limits the growth rates of both phytoplankton and seagrass. Chromophoric dissolved organic matter (CDOM), phytoplankton, tripton (inanimate particulate matter) and seawater itself are the major factors that influence light attenuation. These factors influence light attenuation via absorption and/or scattering of photons. In different ecosystems various combinations of these factors dominate the light regime. The light attenuation coefficient or Kt provides a useful way to quantify and compare the light regime in different regions. This coefficient can be further partitioned into partial light attenuation coefficients for each of the above factors.
Florida Bay is an area that has undergone numerous ecological changes over the past two decades. The most widely publicized of these was the mass seagrass die-off that began in 1987. Although the cause of this die-off is still unresolved it has been theorized that the die-off adversely affected the light regime of the Bay directly via resuspension of sediments and indirectly if greater nutrient availability led to increased phytoplankton biomass. The light environment of Florida Bay was studied intensely from August 1993 until September of 1994 by Phlips et al. 1995. However, not only has considerable time elapsed but since 1994 the Bay has significantly changed; in particular, the dense cyanobacterial bloom regularly observed in the central Bay in the early 1990’s is not at present a dominant feature. While there have been some recent light measurements these have been of comparatively limited spatial extent and have not attempted to partition the factors contributing to attenuation. We here report the results of a spatially high resolution study of light attenuation and its components in Florida Bay including both the dry and wet seasons.
A total of eight cruises were conducted at approximately monthly intervals from July 2001 to March 2002 by the R/V Virginia K from NOAA’s Atlantic Oceanographic and Meteorological Laboratory. Hydrographic data from the survey cruises are made available to the community via the world-wide web [www.aoml.noaa.gov/ocd/sferpm/surveymaps.html]. The Virginia K is equipped with a flow-through measurement system consisting of a Seabird model 21 thermosalinograph, a Japan Radio Corporation DGPS 200, a Seapoint CDOM fluorometer, a Seapoint chlorophyll a fluorometer, and a Wetlabs transmissometer. All data are recorded at seven-second intervals. In addition forty discrete stations were sampled each cruise for chlorophyll a (an estimator of phytoplankton abundance), Total Suspended Solids (TSS), and light attenuation (Kt).
Using the station data statistical models were generated to estimate chlorophyll a, tripton (a function of TSS and chlorophyll a), and, eventually Kt, from the underway measurements. To estimate chlorophyll a and tripton both non-linear and linear regression analyses were attempted using the five underway measurements as the independent variables. Linear regression models proved as efficient as non-linear models and multiple regression models were somewhat better than simple regression models. The models estimating chlorophyll a and tripton from underway measurements explained over eighty percent of the variance.
Two different approaches were employed to model Kt. The first was mechanistic and based upon partial light attenuation coefficients for each factor. The concentrations estimated from the underway measurements were multiplied by the specific absorption coefficient (SAC) for each variable (determined from the literature or in the case of tripton by difference). These partial attenuation coefficients were then summed to get an overall light attenuation coefficient. This model explained 74.9% of the variance in Kt. However, there are some a priori weaknesses in this approach that may result in an underestimation of the influence of phytoplankton pigments and an overestimation of the influence of tripton.
The second approach to modeling Kt was statistical (specifically multiple regression analysis). This relaxes some of the constraints of the previous model allowing the regression analysis to estimate “pseudo-SACs” for each factor. Some of these “pseudo-SACs” differed greatly from the previously employed SACs. However, these “pseudo-SACs” were in fact comparable to those calculated using a similar approach in Chesapeake Bay. Some differences are to be expected due to differences in phytoplankton species composition, CDOM chromophores, and the particle size and composition of suspended sediments.
F igure 1. Plot of the mean light attenuation coefficient, Kt, estimated via the mechanistic model from the underway data sets.
While the multiple regression model was marginally more efficient, the two models predicted similar values. In fact, a 0.944 coefficient of determination was found when the results from the two models were regressed upon each other. The mechanistic model was then used to generate high-density maps of attenuation in the Bay from the underway data sets. The mean of these maps is shown in figure 1.
The results of this analysis were then used to determine the sub-areas in the Bay where light-limitation is sufficient to limit phytoplankton primary production and seagrass growth. The light attenuation relationship generated, as well as the partial attenuation coefficients can be used in subsequent water quality models and for ground-truth of satellite remote sensing. NOAA/AOML expects to continue this monthly monitoring over the coming decades as CERP is implemented.
Christopher Kelble, CIMAS-NOAA/AOML/OCD, 4301 Rickenbacker Causeway, Miami, FL 33149